Advanced Game-Theoretic Frameworks for Multi-Agent AI Challenges: A 2025 Outlook
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911016435056640 |
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| author | Malinovskiy, Pavel |
| author_facet | Malinovskiy, Pavel |
| contents | This paper presents a substantially reworked examination of how advanced game-theoretic paradigms can serve as a foundation for the next-generation challenges in Artificial Intelligence (AI), forecasted to arrive in or around 2025. Our focus extends beyond traditional models by incorporating dynamic coalition formation, language-based utilities, sabotage risks, and partial observability. We provide a set of mathematical formalisms, simulations, and coding schemes that illustrate how multi-agent AI systems may adapt and negotiate in complex environments. Key elements include repeated games, Bayesian updates for adversarial detection, and moral framing within payoff structures. This work aims to equip AI researchers with robust theoretical tools for aligning strategic interaction in uncertain, partially adversarial contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced Game-Theoretic Frameworks for Multi-Agent AI Challenges: A 2025 Outlook Malinovskiy, Pavel Multiagent Systems Artificial Intelligence I.2.11; F.2.2 This paper presents a substantially reworked examination of how advanced game-theoretic paradigms can serve as a foundation for the next-generation challenges in Artificial Intelligence (AI), forecasted to arrive in or around 2025. Our focus extends beyond traditional models by incorporating dynamic coalition formation, language-based utilities, sabotage risks, and partial observability. We provide a set of mathematical formalisms, simulations, and coding schemes that illustrate how multi-agent AI systems may adapt and negotiate in complex environments. Key elements include repeated games, Bayesian updates for adversarial detection, and moral framing within payoff structures. This work aims to equip AI researchers with robust theoretical tools for aligning strategic interaction in uncertain, partially adversarial contexts. |
| title | Advanced Game-Theoretic Frameworks for Multi-Agent AI Challenges: A 2025 Outlook |
| topic | Multiagent Systems Artificial Intelligence I.2.11; F.2.2 |
| url | https://arxiv.org/abs/2506.17348 |